ArticleScientific reports2026
White-box modeling of asphaltene precipitation during natural depletion of oil reservoirs.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 authors.
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Abstract
Asphaltene precipitation and deposition pose significant challenges in the oil industry, leading to well blockage, formation damage, impairment of process equipment, and reduced reservoir permeability, which ultimately affect oil production and economic efficiency. Traditional laboratory-based methods for detecting asphaltene precipitation are often costly and time-consuming, emphasizing the need for rapid and reliable predictive techniques. This study develops predictive models for asphaltene precipitation using three white-box machine learning algorithms: Group Method of Data Handling (GMDH), Gene Expression Programming (GEP), and Genetic Programming (GP). The models were trained and validated based on a dataset of 308 laboratory measurements from 25 types of crude oils, considering relevant parameters influencing model performance. Their predictive capability was benchmarked against a thermodynamically consistent cubic-plus-association (CPA) equation of state developed in this work. Statistical evaluations confirmed the reliability of the developed models. Among the correlations, GMDH demonstrated the best performance, achieving the correlation coefficient (R
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